diff --git a/examples/benchmarks/geolifeclef/geolifeclef2024_pre_extracted/config/glc24_cnn_multimodal_ensemble_habitat.yaml b/examples/benchmarks/geolifeclef/geolifeclef2024_pre_extracted/config/glc24_cnn_multimodal_ensemble_habitat.yaml
index 6a9965c2..4fbc611a 100644
--- a/examples/benchmarks/geolifeclef/geolifeclef2024_pre_extracted/config/glc24_cnn_multimodal_ensemble_habitat.yaml
+++ b/examples/benchmarks/geolifeclef/geolifeclef2024_pre_extracted/config/glc24_cnn_multimodal_ensemble_habitat.yaml
@@ -3,8 +3,8 @@ hydra:
     dir: outputs/${hydra.job.name}/${now:%Y-%m-%d_%H-%M-%S}
 
 run:
-  predict: false
-  checkpoint_path: # "outputs/glc24_cnn_multimodal_ensemble_habitat/2024-08-30_20-21-26_multiclass/last.ckpt"
+  predict: true
+  checkpoint_path: "outputs/glc24_cnn_multimodal_ensemble_habitat/2024-09-10_01-42-06_multiclass_correct_split/last.ckpt"
 
 data:
   root: "dataset/geolifeclef-2024_habitats/"
@@ -18,8 +18,8 @@ data:
       bioclim_data_dir:  "${data.root}TimeSeries-Cubes/TimeSeries-Cubes/GLC24-PA-test-bioclimatic_monthly/"
       sentinel_data_dir:  "${data.root}PA_Test_SatellitePatches_RGB/pa_test_patches_rgb/"
   metadata_paths:
-    train:  "${data.root}GLC24_PA_metadata_habitats-lvl3_train_split-10.0%_train.csv"
-    val:  "${data.root}GLC24_PA_metadata_habitats-lvl3_train_split-10.0%_val.csv"
+    train:  "${data.root}GLC24_PA_metadata_habitats-lvl3_train_2_split-10.0%_train.csv"
+    val:  "${data.root}GLC24_PA_metadata_habitats-lvl3_train_2_split-10.0%_val.csv"
     test:  "${data.root}GLC24_PA_metadata_habitats-lvl3_test.csv"
   num_classes: &num_classes 174
   download_data: True
diff --git a/examples/benchmarks/geolifeclef/geolifeclef2024_pre_extracted/evaluate_inference_MME_habitat.py b/examples/benchmarks/geolifeclef/geolifeclef2024_pre_extracted/evaluate_inference_MME_habitat.py
index 6f61134d..dd6c7427 100644
--- a/examples/benchmarks/geolifeclef/geolifeclef2024_pre_extracted/evaluate_inference_MME_habitat.py
+++ b/examples/benchmarks/geolifeclef/geolifeclef2024_pre_extracted/evaluate_inference_MME_habitat.py
@@ -27,7 +27,7 @@
 BOLD = "\033[1m"
 
 # 0. Load data
-df = pd.read_csv('GLC24_habitat_predictions_multiclass_val-dataset.csv')
+df = pd.read_csv('predictions_and_evaluation/predictions_GLC24_SOLUTION_FILE.csv')
 df['target_habitat_id'] = df['target_habitat_id'].astype(str)
 df_gt = df.copy()
 df_preds = df.copy()
@@ -104,12 +104,6 @@
 
 elif TASK == 'multiclass':
     # 1. Convert data to usable types and compute one-hot encodings
-    res = pd.DataFrame(columns=['Accuracy',
-                                'Precision_micro', 'Recall_micro', 'F1_micro',
-                                'Precision_samples', 'Recall_samples', 'F1_samples',
-                                'Precision_macro', 'Recall_macro', 'F1_macro',
-                                'AUC_micro', 'AUC_samples', 'AUC_macro'])
-
     idx = np.arange(len(all_predictions_topk_oh)).reshape(-1, 1)
     all_targets_oh[idx, targets] = 1  # One-hot encode the targets
     all_probas = probas[idx, np.argsort(preds, axis=1)]  # Sort the probabilities in class order
@@ -135,6 +129,6 @@
         print(f"Top-{topk} Accuracy_multiclass: {acc}")
 
     # 4. Save results
-    res.loc[0] = prfs | accs
+    res = pd.DataFrame({k: [v] for k, v in (prfs | accs).items()})
     res.to_csv('Inference_PRC-ACC.csv', index=False)
     print('\nResults saved to Inference_PRC-ACC.csv')
diff --git a/examples/benchmarks/geolifeclef/geolifeclef2024_pre_extracted/predictions_and_evaluation/Inference_PRC-ACC_habitats_lvl3_predictions_correct_split.csv b/examples/benchmarks/geolifeclef/geolifeclef2024_pre_extracted/predictions_and_evaluation/Inference_PRC-ACC_habitats_lvl3_predictions_correct_split.csv
new file mode 100644
index 00000000..431b7727
--- /dev/null
+++ b/examples/benchmarks/geolifeclef/geolifeclef2024_pre_extracted/predictions_and_evaluation/Inference_PRC-ACC_habitats_lvl3_predictions_correct_split.csv
@@ -0,0 +1,2 @@
+Precision_micro_top-1,Recall_micro_top-1,F1_micro_top-1,Precision_samples_top-1,Recall_samples_top-1,F1_samples_top-1,Precision_macro_top-1,Recall_macro_top-1,F1_macro_top-1,Precision_micro_top-3,Recall_micro_top-3,F1_micro_top-3,Precision_samples_top-3,Recall_samples_top-3,F1_samples_top-3,Precision_macro_top-3,Recall_macro_top-3,F1_macro_top-3,Precision_micro_top-5,Recall_micro_top-5,F1_micro_top-5,Precision_samples_top-5,Recall_samples_top-5,F1_samples_top-5,Precision_macro_top-5,Recall_macro_top-5,F1_macro_top-5,Accuracy_multiclass_top-1,Accuracy_multiclass_top-3,Accuracy_multiclass_top-5
+0.337773042088439,0.337773042088439,0.337773042088439,0.337773042088439,0.337773042088439,0.337773042088439,0.20480480687172334,0.18039238248849737,0.1588256540889949,0.17812111525483929,0.5343633457645178,0.2671816728822589,0.17812111525483929,0.5343633457645178,0.2671816728822589,0.11471821920023804,0.33961951262661966,0.14604822765115621,0.12360149174214172,0.6180074587107086,0.20600248623690287,0.1236014917421417,0.6180074587107086,0.20600248623690284,0.07785807970266735,0.41477541996937306,0.11795281024254056,0.337773042088439,0.5343633457645178,0.6180074587107086